Developing collective operations for a parallel computer

ABSTRACT

Developing collective operations for a parallel computer that includes compute nodes includes: presenting, by a collective development tool, a graphical user interface (‘GUI’) to a collective developer; receiving, by the collective development tool from the collective developer through the GUI, a selection of one or more collective primitives; receiving, by the collective development tool from the collective developer through the GUI, a specification of a serial order of the collective primitives and a specification of input and output buffers for each collective primitive; and generating, by the collective development tool in dependence upon the selection of collective primitives, the serial order of the collective primitives, and the input and output buffers for each collective primitive, executable code that carries out the collective operation specified by the collective primitives.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 13/369,451, filed on Feb. 9, 2012.

BACKGROUND OF THE INVENTION

Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for developing collective operationsfor a parallel computer that includes a number of compute nodes.

Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same task (split up and specially adapted) on multiple processorsin order to obtain results faster. Parallel computing is based on thefact that the process of solving a problem usually can be divided intosmaller tasks, which may be carried out simultaneously with somecoordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In sucha manner, a torus network lends itself to point to point operations. Ina tree network, the nodes typically are connected into a binary tree:each node has a parent, and two children (although some nodes may onlyhave zero children or one child, depending on the hardwareconfiguration). Although a tree network typically is inefficient inpoint to point communication, a tree network does provide high bandwidthand low latency for certain collective operations, message passingoperations where all compute nodes participate simultaneously, such as,for example, an allgather operation. In computers that use a torus and atree network, the two networks typically are implemented independentlyof one another, with separate routing circuits, separate physical links,and separate message buffers.

SUMMARY OF THE INVENTION

Methods, apparatus, and products for developing collective operationsfor a parallel computer comprising a plurality of compute nodes aredisclosed in this specification. Developing such collective operationsin accordance with embodiments of the present invention includes:presenting, by a collective development tool, a graphical user interface(GUI) to a collective developer; receiving, by the collectivedevelopment tool from the collective developer through the GUI, aselection of one or more collective primitives; receiving, by thecollective development tool from the collective developer through theGUI, a specification of a serial order of the collective primitives anda specification of input and output buffers for each collectiveprimitive; and generating, by the collective development tool independence upon the selection of collective primitives, the serial orderof the collective primitives, and the input and output buffers for eachcollective primitive, executable code that carries out the collectiveoperation specified by the collective primitives.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for developing collectiveoperations for a parallel computer according to embodiments of thepresent invention.

FIG. 2 sets forth a block diagram of an example compute node useful in aparallel computer configured for developing collective operationsaccording to embodiments of the present invention.

FIG. 3A sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for developing collective operations for a parallelcomputer according to embodiments of the present invention.

FIG. 3B sets forth a block diagram of an example Global CombiningNetwork Adapter useful in systems for developing collective operationsfor a parallel computer according to embodiments of the presentinvention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of developing collective operations for a parallelcomputer according to embodiments of the present invention.

FIG. 5 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of developing collectiveoperations for a parallel computer according to embodiments of thepresent invention.

FIG. 6 sets forth a flow chart illustrating an example method fordeveloping collective operations for a parallel computer according toembodiments of the present invention.

FIG. 7 sets forth a line drawing of an example graphical user interfacepresented by a collective development tool that supports developingcollective operations for a parallel computer according to embodimentsof the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and products for developing collectiveoperations for a parallel computer in accordance with the presentinvention are described with reference to the accompanying drawings,beginning with FIG. 1. FIG. 1 illustrates an exemplary system for whichcollective operations may be developed and within which collectiveoperation may be developed according to embodiments of the presentinvention. The system of FIG. 1 includes a parallel computer (100),non-volatile memory for the computer in the form of a data storagedevice (118), an output device for the computer in the form of a printer(120), and an input/output device for the computer in the form of acomputer terminal (122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a global combining network (106)which is optimized for collective operations using a binary tree networktopology, and a point-to-point network (108), which is optimized forpoint-to-point operations using a torus network topology. The globalcombining network (106) is a data communications network that includesdata communications links connected to the compute nodes (102) so as toorganize the compute nodes (102) as a binary tree. Each datacommunications network is implemented with data communications linksamong the compute nodes (102). The data communications links providedata communications for parallel operations among the compute nodes(102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use insystems configured according to embodiments of the present inventioninclude MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM wasdeveloped by the University of Tennessee, The Oak Ridge NationalLaboratory and Emory University. MPI is promulgated by the MPI Forum, anopen group with representatives from many organizations that define andmaintain the MPI standard. MPI at the time of this writing is a de factostandard for communication among compute nodes running a parallelprogram on a distributed memory parallel computer. This specificationsometimes uses MPI terminology for ease of explanation, although the useof MPI as such is not a requirement or limitation of the presentinvention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process' receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node.

Such example are for explanation only, however, and not for limitation.Each I/O node provides I/O services between compute nodes (102) of itsprocessing set and a set of I/O devices. In the example of FIG. 1, theI/O nodes (110, 114) are connected for data communications I/O devices(118, 120, 122) through local area network (‘LAN’) (130) implementedusing high-speed Ethernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 1 supports collective operationsdeveloped in accordance with embodiments of the present invention. Suchoperations may be developed on a compute node (102) of the parallelcomputer (100) itself or on other automated computing machinery.Developing collective operations in accordance with embodiments of thepresent invention includes: presenting, by a collective developmenttool, a graphical user interface (‘GUI’) to a collective developer. Theterm collective developer is used in this specification to refer to auser (128) that develops software executable on a parallel computer thatcarries out collective operations. A collective development tool is amodule of computer program instructions that, when executed, causeautomated computing machinery comprising an aggregation of computerhardware and software to carry out collective development in accordancewith embodiments of the present invention.

The collective development tool also receives, from the collectivedeveloper through the GUI, a selection of one or more collectiveprimitives. A collective primitive is a module of computer programinstructions that, when executed, carries out a predefined collectivetask. Each primitive is a building block that may be combined with otherprimitives in various ways to form a complex collective operation.Examples of such collective primitives include:

-   -   a multi-sync primitive that, when executed, carriers out        synchronization among a plurality of compute nodes;    -   a multi-cast primitive that, when executed, sends a message to a        group of nodes in parallel;    -   a multi-combine primitive that, when executed, performs an        operation on data received from more than one compute node; and    -   a many-to-many primitive that, when executed, sends unique date        to a group of compute nodes and receives data from another group        of compute nodes.

The collective development tool, after receiving a specification of oneor more collective primitives, also receives, from the collectivedeveloper through the GUI, a specification of a serial order of thecollective primitives and a specification of input and output buffersfor each collective primitive. The order of the collective primitivesspecifies an order of execution of the collective primitives, onefollowing another. The specification of the input and output buffers maybe implemented in various ways, including, for example, as a definitionof a global variable or pointer representing an array of a particularsize.

The collective development tool then generates executable code thatcarries out the collective operation specified by the collectiveprimitives in dependence upon the selection of collective primitives,the serial order of the collective primitives, and the input and outputbuffers for each collective primitive.

The collective development tool enables a collective developer todevelop a collective operation in a graphical interface rather thanthrough tedious composition of source code. In this way, complexcollective operations may be ‘built’ by a developer quickly andefficiently without requiring the developer to compose every computerprogram instructions that will be executed to effect the collectiveoperation.

Developing collective operations for a parallel computer according toembodiments of the present invention is generally implemented for aparallel computer that includes a plurality of compute nodes organizedfor collective operations through at least one data communicationsnetwork. Further, developing collective operations for a parallelcomputer may also be carried in such a parallel computer. In fact, suchcomputers may include thousands of compute nodes. Each compute node isin turn itself a kind of computer composed of one or more computerprocessing cores, its own computer memory, and its own input/outputadapters. For further explanation, therefore, FIG. 2 sets forth a blockdiagram of an example compute node (102) useful in a parallel computercapable of developing collective operations according to embodiments ofthe present invention. The compute node (102) of FIG. 2 includes aplurality of processing cores (165) as well as RAM (156). The processingcores (165) of FIG. 2 may be configured on one or more integratedcircuit dies. Processing cores (165) are connected to RAM (156) througha high-speed memory bus (155) and through a bus adapter (194) and anextension bus (168) to other components of the compute node. Stored inRAM (156) is an application program (159), a module of computer programinstructions that carries out parallel, user-level data processing usingparallel algorithms.

Also stored RAM (156) is a parallel communications library (161), alibrary of computer program instructions that carry out parallelcommunications among compute nodes, including point-to-point operationsas well as collective operations. A library of parallel communicationsroutines may be developed from scratch for use in systems according toembodiments of the present invention, using a traditional programminglanguage such as the C programming language, and using traditionalprogramming methods to write parallel communications routines that sendand receive data among nodes on two independent data communicationsnetworks. Alternatively, existing prior art libraries may be improved tooperate according to embodiments of the present invention. Examples ofprior-art parallel communications libraries include the ‘Message PassingInterface’ (‘MPI’) library and the ‘Parallel Virtual Machine’ (‘PVM’)library.

Also stored in RAM (156) of the compute node (102) is a collectivedevelopment tool (228), a module of computer program instructions that,when executed, cause the compute node (102) to support collectiveoperations development in accordance with embodiments of the presentinvention. Readers of skill in the art will recognize the collectivedevelopment tool (228) of FIG. 2 executes on a compute node for purposesof explanation only, not limitation. Collective development toolsconfigured in accordance with embodiments of the present invention mayexecute on any automated computing machinery.

The collective development tool (228) of FIG. 2 supports collectiveoperation development in accordance with embodiments of the presentinvention by: presenting a GUI (230) to a collective developer;receiving, from the collective developer through the GUI (230), aselection (232) of one or more collective primitives; receiving, fromthe collective developer through the GUI (230), a specification (234) ofa serial order of the collective primitives and a specification (236) ofinput and output buffers for each collective primitive; and generatingexecutable code (238) that carries out the collective operationspecified by the collective primitives in dependence upon the selection(232) of collective primitives, the serial order (234) of the collectiveprimitives, and the input and output buffers (236) for each collectiveprimitive. In some example embodiments, the executable code (238) may bea included as a library function in a parallel communications library(161), as a module of a parallel application (226), or in other ways aswill occur to readers of skill in the art.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (102) of FIG. 2, another factor that decreases the demandson the operating system. The operating system (162) may therefore bequite lightweight by comparison with operating systems of generalpurpose computers, a pared down version as it were, or an operatingsystem developed specifically for operations on a particular parallelcomputer. Operating systems that may usefully be improved, simplified,for use in a compute node include UNIX™, Linux™, Windows XP™, AIX™,IBM's i5/OS™, and others as will occur to those of skill in the art.

The example compute node (102) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in apparatus in a parallel computer forwhich collective operations are developed in accordance with embodimentsof the present invention include modems for wired communications,Ethernet (IEEE 802.3) adapters for wired network communications, and802.11b adapters for wireless network communications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenientalternative access point into the system. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processing core, itsown memory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processing core registers andmemory in compute node (102) for use in dynamically reassigning aconnected node to a block of compute nodes useful in systems for whichcollective operations are developed in accordance with embodiments ofthe present invention to embodiments of the present invention.

The data communications adapters in the example of FIG. 2 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) that is optimal forpoint-to-point message passing operations such as, for example, anetwork configured as a three-dimensional torus or mesh. ThePoint-To-Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186).

The data communications adapters in the example of FIG. 2 include aGlobal Combining Network Adapter (188) that couples example compute node(102) for data communications to a global combining network (106) thatis optimal for collective message passing operations such as, forexample, a network configured as a binary tree. The Global CombiningNetwork Adapter (188) provides data communications through threebidirectional links for each global combining network (106) that theGlobal Combining Network Adapter (188) supports. In the example of FIG.2, the Global Combining Network Adapter (188) provides datacommunications through three bidirectional links for global combiningnetwork (106): two to children nodes (190) and one to a parent node(192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput. The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 3A sets forth a block diagram of anexample Point-To-Point Adapter (180) useful in systems for whichcollective operations are developed in accordance with embodiments ofthe present invention according to embodiments of the present invention.The Point-To-Point Adapter (180) is designed for use in a datacommunications network optimized for point-to-point operations, anetwork that organizes compute nodes in a three-dimensional torus ormesh. The Point-To-Point Adapter (180) in the example of FIG. 3Aprovides data communication along an x-axis through four unidirectionaldata communications links, to and from the next node in the −x direction(182) and to and from the next node in the +x direction (181). ThePoint-To-Point Adapter (180) of FIG. 3A also provides data communicationalong a y-axis through four unidirectional data communications links, toand from the next node in the −y direction (184) and to and from thenext node in the +y direction (183). The Point-To-Point Adapter (180) ofFIG. 3A also provides data communication along a z-axis through fourunidirectional data communications links, to and from the next node inthe −z direction (186) and to and from the next node in the +z direction(185).

For further explanation, FIG. 3B sets forth a block diagram of anexample Global Combining Network Adapter (188) useful in systems for[for which collective operations are developed in accordance withembodiments of the present invention. The Global Combining NetworkAdapter (188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. The Global Combining Network Adapter (188) inthe example of FIG. 3B provides data communication to and from childrennodes of a global combining network through four unidirectional datacommunications links (190), and also provides data communication to andfrom a parent node of the global combining network through twounidirectional data communications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems for which collectiveoperations are developed in accordance with embodiments of the presentinvention. In the example of FIG. 4, dots represent compute nodes (102)of a parallel computer, and the dotted lines between the dots representdata communications links (103) between compute nodes. The datacommunications links are implemented with point-to-point datacommunications adapters similar to the one illustrated for example inFIG. 3A, with data communications links on three axis, x, y, and z, andto and fro in six directions +x (181), −x (182), +y (183), −y (184), +z(185), and −z (186). The links and compute nodes are organized by thisdata communications network optimized for point-to-point operations intoa three dimensional mesh (105). The mesh (105) has wrap-around links oneach axis that connect the outermost compute nodes in the mesh (105) onopposite sides of the mesh (105). These wrap-around links form a torus(107). Each compute node in the torus has a location in the torus thatis uniquely specified by a set of x, y, z coordinates. Readers will notethat the wrap-around links in the y and z directions have been omittedfor clarity, but are configured in a similar manner to the wrap-aroundlink illustrated in the x direction. For clarity of explanation, thedata communications network of FIG. 4 is illustrated with only 27compute nodes, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations for use in a system forwhich collective operations are developed in accordance with embodimentsof the present invention may contain only a few compute nodes or maycontain thousands of compute nodes. For ease of explanation, the datacommunications network of FIG. 4 is illustrated with only threedimensions, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations for use in a system forwhich collective operations are developed in accordance with embodimentsof the present may in facet be implemented in two dimensions, fourdimensions, five dimensions, and so on. Several supercomputers now usefive dimensional mesh or torus networks, including, for example, IBM'sBlue Gene Q™.

For further explanation, FIG. 5 sets forth a line drawing illustratingan example global combining network (106) useful in systems for whichcollective operations are developed in accordance with embodiments ofthe present invention. The example data communications network of FIG. 5includes data communications links (103) connected to the compute nodesso as to organize the compute nodes as a tree. In the example of FIG. 5,dots represent compute nodes (102) of a parallel computer, and thedotted lines (103) between the dots represent data communications linksbetween compute nodes. The data communications links are implementedwith global combining network adapters similar to the one illustratedfor example in FIG. 3B, with each node typically providing datacommunications to and from two children nodes and data communications toand from a parent node, with some exceptions. Nodes in the globalcombining network (106) may be characterized as a physical root node(202), branch nodes (204), and leaf nodes (206). The physical root (202)has two children but no parent and is so called because the physicalroot node (202) is the node physically configured at the top of thebinary tree. The leaf nodes (206) each has a parent, but leaf nodes haveno children. The branch nodes (204) each has both a parent and twochildren. The links and compute nodes are thereby organized by this datacommunications network optimized for collective operations into a binarytree (106). For clarity of explanation, the data communications networkof FIG. 5 is illustrated with only 31 compute nodes, but readers willrecognize that a global combining network (106) optimized for collectiveoperations for use in systems for which collective operations aredeveloped in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation according toembodiments of the present invention. Using the rank to identify a nodeassumes that only one such task is executing on each node. To the extentthat more than one participating task executes on a single node, therank identifies the task as such rather than the node. A rank uniquelyidentifies a task's location in the tree network for use in bothpoint-to-point and collective operations in the tree network. The ranksin this example are assigned as integers beginning with 0 assigned tothe root tasks or root node (202), 1 assigned to the first node in thesecond layer of the tree, 2 assigned to the second node in the secondlayer of the tree, 3 assigned to the first node in the third layer ofthe tree, 4 assigned to the second node in the third layer of the tree,and so on. For ease of illustration, only the ranks of the first threelayers of the tree are shown here, but all compute nodes in the treenetwork are assigned a unique rank.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method developing collective operations for a parallel computeraccording to embodiments of the present invention. The parallel computerfor which collective operations are developed in accordance with themethod of FIG. 6 may include a plurality of compute nodes, similar tothose set forth in the example of FIGS. 1 and 2.

The method of FIG. 6 includes presenting (602), by a collectivedevelopment tool, a graphical user interface (‘GUI’) to a collectivedeveloper. The GUI presented to the collective developer may includemany different GUI objects such as: graphical shapes representingcollective primitives; graphical connectors for specifying a serialorder among the collective primitives; drop down lists, text inputfields, and the like for specifying size, type, and variable names ofinput and output buffers, and so on as will occur to readers of skill inthe art. Presenting such a GUI may be carried out by displaying the GUIon a display screen, such as a monitor.

The method of FIG. 6 also includes receiving (604), by the collectivedevelopment tool from the collective developer through the GUI, aselection of one or more collective primitives. Receiving (604) aselection of one or more collective primitives may be carried out invarious ways, including, for example by detecting user input deviceactivity that indicates a selection of one or more graphical iconsrelated to the collective primitives, by receiving input from user inputdevices (as keyboard, mouse, and microphone, and the like), thatdrags-and-drops a GUI object representing a collective primitive, thatinvokes a selection of a primitive in a drop-down selection list, and soon as will occur to readers of skill in the art.

From the collective developer's perspective, a collective primitiveincludes an input, an operation, and an output. From the perspective ofthe collective development tool, a collective primitive is a module ofcomputer program instructions, source code in some embodiments. Examplesof such collective primitives include:

-   -   a multi-sync primitive that, when executed, carriers out        synchronization among a plurality of compute nodes;    -   a multi-cast primitive that, when executed, sends a message to a        group of nodes in parallel;    -   a multi-combine primitive that, when executed, performs an        operation on data received from more than one compute node; and    -   a many-to-many primitive that, when executed, sends unique date        to a group of compute nodes and receives data from another group        of compute nodes.

The method of FIG. 6 also includes receiving (606), by the collectivedevelopment tool from the collective developer through the GUI, aspecification of a serial order of the collective primitives and aspecification of input and output buffers for each collective primitive.Receiving (606) a specification of a serial order of the collectiveprimitives may be carried out in various ways including, for example, byreceiving input from user input devices that drags-and-drops a GUIconnector between two GUI objects representing collective primitives,that inputs text in a text field specifying the order, that selects anorder form a drop-down selection list, and in other ways as will occurto readers of skill in the art.

The method of FIG. 6 also includes receiving (606) a specification ofinput and output buffers for each collective primitive may be carriedout by receiving input from user input devices that defines a size, anumber of elements (or number of compute nodes), a variable name orpointer representing each buffer, and so on.

The method of FIG. 6 also includes generating (608), by the collectivedevelopment tool in dependence upon the selection of collectiveprimitives, the serial order of the collective primitives, and the inputand output buffers for each collective primitive, executable code thatcarries out the collective operation specified by the collectiveprimitives. Generating (608) executable code may be carried out byinserting in an executable file, modules of computer programinstructions for each collective primitive (‘collective primitivemodules’) and, in accordance with predefined rules, insert ‘gluemodules’ between the collective primitive modules. A ‘glue module’ asthe term is used in this specification refers to a module of computerprogram instructions configured to be inserted between collectiveprimitive modules for the purpose of linking the collective primitivemodules during execution of the collective operation. Predefined rulesthat specify glue modules to be inserted between collective primitivemodules may be based on many factors of the parallel computer upon whichthe collective operation is to be carried out. Different glue modulesfor example may be associated with different collective primitives,different network topologies, compute node architectures, number ofcompute nodes in an operational group, memory resources forcommunication adapters of compute nodes, and so on. In some embodiments,the collective development tool is configured with such parallelcomputer characteristics prior to collective operation development. Inother embodiments, the collective developer may provide such parallelcomputer characteristics to the collective development tool whiledeveloping the collective operation.

The collective development tool may insert such glue modules betweencollective primitive modules automatically, without the collectivedeveloper's interaction. In this way, the collective operation may beproduced without a collective developer composing any source code. Thecollective development tool therefore provides a collective developerwith a tool to easily construct a collective operation, while alsoproviding the collective developer means by which to specify, in finedetail, portions of the collective operation. Once the executable codeis generated by the collective development tools, the code may bedeployed on a parallel computer and executed.

FIG. 7 sets forth a line drawing of an example graphical user interfacepresented by a collective development tool that supports developingcollective operations for a parallel computer according to embodimentsof the present invention. The example GUI (702) of FIG. 7 includes twopanes: a collective primitives pane and a collective operation pane. Thecollective primitives pane includes a graphical icon for each collectiveprimitive available for selection by a collective developer (a user). Inthe example of FIG. 7, the collective primitives pane includes:

-   -   a graphical icon representing a multi-sync primitive (704) that,        when executed, carriers out synchronization among a plurality of        compute nodes;    -   a graphical icon representing a multi-cast primitive (705) that,        when executed, sends a message to a group of nodes in parallel;    -   a graphical icon representing a multi-combine primitive (706)        that, when executed, performs an operation on data received from        more than one compute node; and    -   a graphical icon representing a many-to-many primitive (718)        that, when executed, sends unique date to a group of compute        nodes and receives data from another group of compute nodes.

Each of these graphical icons may be selected, dragged, and dropped intothe collective operation pane in any combination and in any number. Inthis example, a collective developer has selected a multi-sync primitive(704), a multi-combine primitive (708) and a many-to-many primitive(718) for a collective operation by dragging the icons corresponding toeach primitive to the collective operation pane. The collectivedeveloper has also specified an ‘OR’ operation to be effected whilecarrying out the multi-combine (708) primitive.

Once the collective developer selects the collective primitives thatwill form the collective operation, the collective developer may thenspecify a serial order of the collective primitives and input and outputbuffers for each selected collective primitive. In the example of FIG.7, the collective developer has specified input buffers (706, 710, 712,716) and output buffer (706, 712, 714). Some buffers may operate as anoutput buffer for one collective operation and an input buffer foranother. A collective developer may also specify other characteristicsof the buffers through text fields or other GUI objects configured toreceive data entry such as a size, a type, a variable or pointerrepresenting the buffer and so on.

The collective developer has also drawn, dragged, or otherwise provideduser input to form arrows (720) between collective primitives andbuffers in order to specify a serial order amongst the collectiveprimitives. In this example, the serial order of the collectiveprimitives begins with the multi-sync (704) primitive, continues withthe multi-combine (708) primitive, and finishes with the many-to-many(718) primitive.

After a collective developer selects collective primitives, specifies anorder of the primitives, and specifies input and output buffers of thecollective primitives, the collective developer may initiate generate ofexecutable code that will carry out the collective operation. A user mayinitiate such generation in various ways through the example GUI (702)of FIG. 7 including, invoking a GUI button designated for suchgeneration, selecting an action from a drop down list or a menu list,entering a predefined set of keyboard keystrokes, and so on as willoccur to readers of skill in the art.

In this way, a collective developer may efficiently and quickly createand generate executable code for a collective operation, while actuallycomposing very little, if any, executable code. In addition to the easeand efficiency of collective development provided to a collectivedeveloper through the collective development tool of the presentinvention, the developer is also provided powerful means by which tospecify characteristics of the collective operation in fine detail.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readabletransmission medium or a computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable transmission medium may include a propagated datasignal with computer readable program code embodied therein, forexample, in baseband or as part of a carrier wave. Such a propagatedsignal may take any of a variety of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable transmission medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A method of developing collective operations fora parallel computer comprising a plurality of compute nodes, the methodcomprising: presenting, by a collective development tool, a graphicaluser interface (‘GUI’) to a collective developer; receiving, by thecollective development tool from the collective developer through theGUI, a selection of a plurality of collective primitives; receiving, bythe collective development tool from the collective developer throughthe GUI, a specification of a serial order of the collective primitives;receiving, by the collective development tool from the collectivedeveloper through the GUI, a specification of an input buffer and anoutput buffer for each collective primitive; and wherein receiving, bythe collective development tool from the collective developer throughthe GUI, the selection of the plurality of collective primitives furthercomprises: detecting, by the collective development tool through theGUI, user input device activity that indicates a selection of one ormore graphical icons related to the collective primitives; generating,by a collective development tool in dependence upon the selection of theplurality of graphical icons related to collective primitives, theserial order of the plurality of collective primitives specifying anorder of execution of the plurality of collective primitives, and thespecification of an input buffer and an output buffer for eachcollective primitive, executable code that carries out the collectiveoperation specified by the collective primitives, including: convertingthe serial order of the collective primitives into an execution order ofthe plurality of collective primitive modules of computer programinstructions; inserting, into an executable file, the plurality ofcollective primitive modules of computer program instructions in theexecution order; and inserting, into the executable file, one or moreglue modules between the plurality of collective primitive modules,wherein each glue module is a module of computer program instructionsconfigured to be inserted between two collective primitive modules forthe purpose of linking the two collective primitive modules duringexecution of the collective operation, wherein each glue module isselected in dependence upon attributes of the parallel computer uponwhich the collective operation is to be carried out, includingrespective collective primitives, respective network topologies, computenode architecture, and number of compute nodes in a computational group.2. The method of claim 1 wherein at least one collective primitivecomprises: a multi-sync primitive that, when executed, carries outsynchronization among a plurality of compute nodes.
 3. The method ofclaim 1 wherein at least one collective primitive comprises: amulti-cast primitive that, when executed, sends a message to a group ofnodes in parallel.
 4. The method of claim 1 wherein at least onecollective primitive comprises: a multi-combine primitive that, whenexecuted, performs an operation on data received from more than onecompute node.
 5. The method of claim 1 wherein at least one collectiveprimitive comprises: a many-to-many primitive that, when executed, sendsunique date to a group of compute nodes and receives data from anothergroup of compute nodes.